Determining the number of factoArs is essential to factor analysis. In this paper, we propose an efficient cross validation (CV) method to determine the number of factors in the approximate factor model. The method applies CV twice, first along the direction of the observations and then the direction of the variables, and hence is referred to hereafter as double cross-validation (DCV). Unlike most CV methods, which are prone to overfitting, DCV is statistically consistent in determining the number of factors when both dimensions of variables and sample size are sufficiently large. Simulation studies show that DCV has outstanding performance in comparison to existing methods in selecting the number of factors, especially when the idiosyncratic error has heteroscedasticity, or heavy tail, or relatively large variance.

6月15日
2:00pm - 3:00pm
地点
Room 2463 (Lifts 25/26)
讲者/表演者
Prof. Yingcun XIA
Department of Statistics and Applied Probability, National University of Singapore
主办单位
Department of Mathematics
联系方法
付款详情
对象
Alumni, Faculty and staff, PG students, UG students
语言
英语
其他活动
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研讨会, 演讲, 讲座
IAS / School of Science Joint Lecture - Leveraging Protein Dynamics Memory with Machine Learning to Advance Drug Design: From Antibiotics to Targeted Protein Degradation
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11月8日
研讨会, 演讲, 讲座
IAS / School of Science Joint Lecture - Some Theorems in the Representation Theory of Classical Lie Groups
Abstract After introducing some basic notions in the representation theory of classical Lie groups, the speaker will explain three results in this theory: the multiplicity one theorem for classical...